Trading platforms and financial instrument current prediction software provide predictive analyses of individual financial instruments or a group of similarly targeted financial instruments based on current valuation and/or performance of the particular financial instrument or its group. Further, such software may also provide prediction for varying time periods from the analysis of current and historic financial data values, where the data values are gathered over several different time periods.
However, it is common that such current prediction software is unable to follow inconsistent changes in the market environment. Specifically, the software may rely on single patterns or single features in the market fluctuations, but may not adapt to irregular fluctuations that are not typical in regular market cycles. By way of an example, current prediction software may only focus on closing prices for a stock and may ignore sudden and unexpected trading volumes. This approach only looks at part of the available information. An unexpected pattern in the closing price for a stock may reflect an existing pattern, such as the release of financial information by the company that issued the stock. This is an expected event, against, for example, a mid-day news release related to the company, that may cause high volume transaction unrelated to the financial information. The closing value may, therefore, mask the influence of other features/attributes of the stock. The unexpected trading volumes may not reflect entirely on stock performance until some time has elapsed, therefore rendering an automated analysis irrelevant until after the opportunity has passed.
In another example, known prediction software uses a single feature of a financial instrument for prediction purposes. The single feature may be the closing price alone or the volume of trade alone. Further, the prediction software may use a single prediction model, such as, a hidden markov model (HMM) or a clustering model to predict future valuations of the financial instrument from current valuations or volumes. However, single prediction models are not accurate and do not track multiple feature changes effectively.
Accordingly, there is a need for a process that is able to identify stock value changes, with high confidence, hours to days in advance of the changes.